Real-time monitoring of volatile organic compounds (VOCs) was conducted in Xinxiang, China, during the implementation of Xinxiang's ozone pollution control period (CP) in June 2021. To evaluate the ...effectiveness of the control measures, three study periods were determined by combining meteorological conditions and the implementation time of the control measures: before, during, and after the CP of ozone pollution (BCP, CP, and ACP, respectively). The average concentrations of VOCs during the three periods were 41.20 ± 4.99 ppbv, 33.64 ± 5.65 ppbv, and 37.42 ± 2.59 ppbv, respectively, with the same top three components, namely oxygenated VOCs (OVOCs), alkanes, and halogenated hydrocarbons (XVOCs). However, the concentrations of these three components decreased substantially during the CP (by 19 %, 18 %, and 11 %, respectively). The ozone formation potential (OFP) during the BCP was 144.47 ppbv, which was 1.2 times and 1.3 times higher than those during the ACP and CP periods, respectively. During the CP, the proportion of alkenes that contributed to the OFP decreased significantly by 24 %. Five types of VOCs sources were determined by positive matrix factorization (PMF): (1) solvent use, (2) biogenic, (3) secondary formation, (4) industrial process, and (5) vehicle exhaust and fuel evaporation sources. The VOCs emissions from industrial processes decreased by 54 % during the CP, whereas those from vehicle exhaust and fuel evaporation sources decreased by 36 %, indicating the effectiveness of emission control measures and the importance of these two sources for VOCs control in Xinxiang. In terms of regional transport, the results of the spatial analysis revealed that Hebi and Anyang in the northeast and Zhengzhou and Pingdingshan in the southwest, affected significantly the VOCs of Xinxiang. These results highlight the importance of controlling VOCs emissions in Xinxiang. Furthermore, attention should be paid to controlling the regional transport of surrounding cities.
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•Characteristics and sources of VOCs during three different periods were analysed in Xinxiang.•Industrial process and vehicle exhaust and fuel evaporation were major VOCs sources.•Regional transport had great influences on the VOCs of Xinxiang.•Emission control measures in June 2021 effectively reduced the VOCs emission.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Family caregivers’ distinct depressive-symptom trajectories are understudied and have been examined independently during end-of-life (EOL) caregiving or bereavement, making it difficult to validate ...two competing hypotheses (wear-and-tear vs. relief) of caregiving effects on bereavement. Existing studies may also miss short-term heterogeneity in depressive symptoms during the immediate postloss period due to lengthy delays in the first postloss assessment.
This secondary-analysis study examined distinct depressive-symptom trajectories for caregivers of advanced cancer patients from EOL caregiving through the first 2 bereavement years with closely spaced assessments.
Depressive symptoms were measured monthly during EOL caregiving and 1, 3, 6, 13, 18, and 24 months postloss among 661 caregivers using the Center for Epidemiologic Studies-Depression scale. Depressive-symptom trajectories were identified using latent-class growth analysis while controlling for gender and age.
We identified seven distinct depressive-symptom trajectories (prevalence) characterized by the timing, intensity, and duration of depressive symptoms: minimal-impact resilience (20.4%), recovery (34.0%), preloss-grief only (21.6%), delayed symptomatic (9.1%), relief (5.9%), prolonged symptomatic (6.5%), and chronically persistent distressed (2.5%).
Caregivers of advanced cancer patients responded heterogeneously to the stresses of EOL caregiving and bereavement. The majority of caregivers was resilient while providing caregiving and quickly rebounded to healthy levels of psychological functioning during bereavement, whereas a minority experienced delayed-symptomatic, prolonged-symptomatic, or chronically-persistent-distressing depressive-symptom trajectories. Linking caregivers’ psychological experiences from caregiving through bereavement by closely spaced assessments can more comprehensively illustrate their depressive-symptom trajectories, which confirm both the wear-and-tear and relief hypotheses, and help in targeting interventions for distinct depressive-symptom trajectories.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Image captioning is a task of generating natural language descriptions for images, which is important and challenging in computer vision. This task involves both visual and linguistic understanding, ...which makes it complex and difficult to solve. In this paper, we propose a novel and efficient image captioning model, named MobileNet V3-Transformer (Mob-Tran), which combines the advantages of both convolutional and transformer architectures. Our model uses the improved MobileNet V3 and the transformer's encoder as the encoder to extract and enhance visual features from images, and uses the transformer's decoder as the decoder to generate captions based on the encoded features. The MobileNet V3 model used in this experiment has had its classifier removed. This experiment combined automatic and human evaluation to evaluate multiple models, including MobileNet V3-Transformer, using ten automatic evaluation metrics (BLEU_1, BLEU_2, BLEU_3, BLEU_4, CIDEr, ROUGE, METEOR, Model Storage Size, Model Training Time and Model Inference Time) and five human evaluation criteria (Grammaticality, Adequacy, Logic, Readability, and Humanness). The experimental results demonstrate that our model is capable of producing high-quality image captions with low complexity and high efficiency.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
Background
Developing accurate prognostic awareness, a cornerstone of preference‐based end‐of‐life (EOL) care decision‐making, is a dynamic process involving more prognostic‐awareness states than ...knowing or not knowing. Understanding the transition probabilities and time spent in each prognostic‐awareness state can help clinicians identify trigger points for facilitating transitions toward accurate prognostic awareness. We examined transition probabilities in distinct prognostic‐awareness states between consecutive time points in 247 cancer patients’ last 6 months and estimated the time spent in each state.
Methods
Prognostic awareness was categorized into four states: (a) unknown and not wanting to know, state 1; (b) unknown but wanting to know, state 2; (c) inaccurate awareness, state 3; and (d) accurate awareness, state 4. Transitional probabilities were examined by multistate Markov modeling.
Results
Initially, 59.5% of patients had accurate prognostic awareness, whereas the probabilities of being in states 1–3 were 8.1%, 17.4%, and 15.0%, respectively. Patients’ prognostic awareness generally remained unchanged (probabilities of remaining in the same state: 45.5%–92.9%). If prognostic awareness changed, it tended to shift toward higher prognostic‐awareness states (probabilities of shifting to state 4 were 23.2%–36.6% for patients initially in states 1–3, followed by probabilities of shifting to state 3 for those in states 1 and 2 9.8%–10.1%). Patients were estimated to spend 1.29, 0.42, 0.68, and 3.61 months in states 1–4, respectively, in their last 6 months.
Conclusion
Terminally ill cancer patients’ prognostic awareness generally remained unchanged, with a tendency to become more aware of their prognosis. Health care professionals should facilitate patients’ transitions toward accurate prognostic awareness in a timely manner to promote preference‐based EOL decisions.
Implications for Practice
Terminally ill Taiwanese cancer patients’ prognostic awareness generally remained stable, with a tendency toward developing higher states of awareness. Health care professionals should appropriately assess patients’ readiness for prognostic information and respect patients’ reluctance to confront their poor prognosis if they are not ready to know, but sensitively coach them to cultivate their accurate prognostic awareness, provide desired and understandable prognostic information for those who are ready to know, and give direct and honest prognostic information to clarify any misunderstandings for those with inaccurate awareness, thus ensuring that they develop accurate and realistic prognostic knowledge in time to make end‐of‐life care decisions.
摘要
背景. 预后意识是做出基于偏好的临终（EOL）关怀决定的基石, 形成准确的预后意识是一个涉及更多预后意识状态的动态过程, 而不仅仅是了解或不了解预后。了解每种预后意识状态的转变概率和时间耗费可以帮助临床医生识别触发因素, 以促进转变为准确的预后意识。研究检测了247名癌症患者在生命最后6个月的连续时间点之间不同预后意识状态的转变概率, 并估计了每种状态的时间耗费。
方法. 预后意识被分为四种状态：（a）状态1：不了解且不想了解；（b）状态2：不了解但想了解；（c）状态3：意识不准确；（d）状态4：意识准确。转变概率通过多维状态马尔科夫模型检测获得。
结果. 最初, 59.5％的患者具有准确的预后意识, 而处于状态1‐3的概率分别为8.1%、17.4%和15.0%。患者的预后意识通常保持不变（保持在同一状态的概率：45.5%–92.9%）。如果患者预后意识改变, 则预后意识倾向于转变为更高的状态（最初处在状态1‐3的患者转变为状态4的概率为23.2％‐36.6％, 处在状态1和2的患者转变为状态3的概率为9.8%–10.1%）。估计这些患者在生命的最后6个月中处于状态1‐4的时间分别为1.29、0.42、0.68和3.61个月。
结论. 终末期癌症患者的预后意识通常比较稳定, 倾向于对其预后的意识越来越准确。医疗保健专业人员应及时帮助患者转变为准确的预后意识, 以使其做出基于偏好的EOL关怀决定。
Accurate prognostic awareness is a cornerstone of preference‐based end‐of‐life care decision‐making for terminally ill cancer patients, promoting optimal quality of care at the end of life. This study explored the transition probabilities in distinct states of prognostic awareness in terminally ill cancer patients in the last 6 months of life.
The extent to which patients' preferences for end-of-life (EOL) care are honored may be distorted if preferences are measured long before death, a common approach of existing research. We examined ...the concordance between cancer patients' states of life-sustaining treatments (LSTs) received in their last month and LST preference states assessed longitudinally over their last six months.
We examined states of preferred and received LSTs (cardiopulmonary resuscitation, intensive care unit care, chest compression, intubation with mechanical ventilation, intravenous nutrition, and nasogastric tube feeding) in 271 cancer patients' last six months by a transition model with hidden Markov modeling (HMM). The extent of concordance was measured by a percentage and a kappa value.
HMM identified four LST preference states: life-sustaining preferring, comfort preferring, uncertain, and nutrition preferring. HMM identified four LST states received in patients' last month: generally received LSTs, LSTs uniformly withheld, selectively received LSTs, and received intravenous nutrition only. LSTs received concurred poorly with patients' preferences estimated right before death (39.5% and kappa value: 0.06 95% CI: −0.02, 0.13). Patients in the life-sustaining–preferring, uncertain, and nutrition-preferring states primarily received no LSTs, and patients in three of four states received intravenous nutrition against their preferences. Concordance was strongest for comfort-preferring patients.
Concordance was poor between patients' preferred and received LST states. Interventions are needed to clarify patients' EOL care goals and to facilitate their understanding about LST's ineffectiveness in prolonging life at EOL. Such interventions might increase patients' comfort preference and ensure concordance between their preferred and received EOL care.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Stability of life-sustaining treatment (LST) preferences at end of life (EOL) has not been well established for terminally ill cancer patients nor have transition probabilities been explored between ...different types of preferences.
We assessed the stability of cancer patients' LST preferences at EOL by identifying distinct LST preference states and examining the probability of each state transitioning to other states between consecutive time points.
Stability of LST preferences (cardiopulmonary resuscitation, intensive care unit ICU care, cardiac massage, intubation with mechanical ventilation, intravenous nutrition support, and nasogastric tube feeding) was examined among 303 cancer patients in their last six months by hidden Markov modeling.
Six distinct LST preference states (initial size) were identified: uniformly preferring (8.3%), uniformly rejecting (33.8%), and uniformly uncertain about (20.5%) LST, favoring intravenous nutrition support but rejecting other treatments (19.9%), and favoring (3.6%) or uncertain about (14.0%) nutrition support and ICU care while rejecting other treatments. Shifts between LST preference states were relatively small between any two time points (transition probability of staying at the same state was 92.1% to 97.5%), except for the state characterized by uncertainty about nutrition support and ICU care while rejecting other treatments, in which 8.3% of patients shifted LST preferences toward uniform uncertainty at a subsequent assessment.
Our patients' LST preferences remained stable without prominent shifts toward preferring less aggressive LSTs even when death approached. Clarifying patients' understanding and expectations about LST efficacy and tailoring interventions to the unique needs of patients in each state may provide personalized EOL care.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Our study addressed important knowledge gaps about trajectories of distinct conjoint symptom-functional states, that is, patterns for different levels of combined symptom distress and functional ...impairment, over cancer patients' last year and their ability to predict survival.
We identified distinct symptom-functional states and explored their changes over 317 terminally ill cancer patients' last year by a transition model using hidden Markov modeling. These distinct symptom-functional states' ability to predict current survival probability, measured in the previous assessment, was evaluated by multivariate Cox regression models.
We identified five worsening, conjoint symptom-functional states: 1) mild symptom distress with high functioning, 2) moderate symptom distress with mild functional impairment, 3) severe symptom distress with moderate functional impairment, 4) moderate symptom distress with severe functional impairment, and 5) profound symptom distress and functional impairment. Trajectories of these five states differed substantially by direction (downward vs. upward) and speed. Participants in States 1–4 had substantially lower risk of subsequent death than those in State 5 (adjusted hazard ratios 95% CI ranged from 0.048 0.028–0.081 to 0.434 0.316–0.579). The risk of subsequent death differed significantly between patients in any two distinct symptom-functional states, except between those in States 3 and 4.
Our identification of five distinct symptom-functional states and their unique transition patterns and prediction of mortality provides all stakeholders with guides for end-of-life care. Goals of end-of-life care should change toward palliative care and effective symptom management for patients with at least moderate symptom distress and substantial functional impairment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose
In this retrospective review of prospectively collected data, we sought to investigate whether early FDG-PET assessment of treatment response based on total lesion glycolysis measured using a ...systemic approach (TLG-S) would be superior to either local assessment with EORTC (European Organization for Research and Treatment of Cancer) criteria or single-lesion assessment with PERCIST (PET Response Criteria in Solid Tumors) for predicting clinical outcomes in patients with metastatic lung adenocarcinoma treated with erlotinib. We also examined the effect of bone flares on tumor response evaluation by single-lesion assessment with PERCIST in patients with metastatic bone lesions.
Methods
We performed a retrospective review of prospectively collected data from 23 patients with metastatic lung adenocarcinoma treated with erlotinib. All participants underwent FDG-PET imaging at baseline and on days 14 and 56 after completion of erlotinib treatment. In addition, diagnostic CT scans were performed at baseline and on day 56. FDG-PET response was assessed with TLG-S, EORTC, and PERCIST criteria. Response assessment based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors) from diagnostic CT imaging was used as the reference standard. Two-year progression-free survival (PFS) and overall survival (OS) served as the main outcome measures.
Results
We identified 13 patients with bone metastases. Of these, four (31 %) with persistent bone uptake due to bone flares on day 14 were erroneously classified as non-responders according to the PERCIST criteria, but they were correctly classified as responders according to both the EORTC and TLG-S criteria. Patients who were classified as responders on day 14 based on TLG-S criteria had higher rates of 2-year PFS (26.7 % vs. 0 %,
P
= 0.007) and OS (40.0 % vs. 7.7 %,
P
= 0.018). Similar rates were observed in patients who showed a response on day 56 based on CT imaging according to the RECIST criteria. Patients classified as responders on day 14 according to the EORTC criteria on FDG-PET imaging had better rates of 2-year OS than did non-responders (36.4 % vs. 8.3 %,
P
= 0.015).
Conclusions
TLG-S criteria may be of greater help in predicting survival outcomes than other forms of assessment. Bone flares, which can interfere with the interpretation of treatment response based on PERCIST criteria, are not uncommon in patients with metastatic lung adenocarcinoma treated with erlotinib.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
The time schedules for response evaluation of epidermal growth factor receptor-tyrosine kinase Inhibitor (EGFR-TKI) in non-small cell lung cancer (NSCLC) patients are still ill-defined.
Stage IIIB/IV ...patients with histologically proven NSCLC were enrolled in this study if the tumor cells bore EGFR mutations other than T790M. Eligible patients were treated with either 250 mg of gefitinib or 150 mg of erlotinib once daily. The early response rate computed tomography (CT) scan on Day 14, definitive response rate determined on Day 56, progression-free survival (PFS), overall survival (OS), and toxicity profile were assessed prospectively.
Thirty-nine patients were enrolled in this study. A total of 29 patients (29/39, 74.4%) achieved partial response (PR). Twenty-one patients (21/39, 53.8%) had early radiological response on Day 14. The early radiological response rate in patients with PR was 72.4% (21/29). Only eight patients without a PR on early CT still ended with PR. Among the 29 patients with PR, the PFS (8.1 months) and OS (18.3 months) of the 21 patients with early CT response were shorter than those of the 8 patients without early CT response (11.9 and 24.0 months for PFS and OS, respectively). But the survival differences were statistically non-significant.
A very high percentage (72.4%, 21/29) of NSCLC patients with EGFR mutations with PR demonstrates early radiological response to EGFR-TKIs, which would advocate early radiological examination for EGFR-TKI therapy in NSCLC patients.
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